import keras
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras import backend as K
from sklearn.model_selection import train_test_split
from keras_applications.resnet import ResNet50
import numpy as np
import cv2
import os

# 需要读取的图像路径
IMAGE_PATH = 'C:\\Users\\duanchen\\Documents\\Tencent Files\\1832931759\\FileRecv\\images\\duanchen'
MODEL_PATH = 'C:\\Users\\duanchen\\Documents\\Tencent Files\\1832931759\\FileRecv\\images\\model'
# 将该路径下的图片全部转换成灰度图像
imgs = []


def read_img(file_pathname):
    # 遍历该目录下的所有图片文件
    for filename in os.listdir(file_pathname):
        img = cv2.imread(file_pathname + '/' + filename)
        # （下面第一行是将RGB转成单通道灰度图，第二步是将单通道灰度图转成3通道灰度图）
        grey = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
        # 改写源文件为灰度图像
        # cv2.imwrite(file_pathname+"/"+filename, grey)
        grey = cv2.resize(grey, (28, 28))
        imgs.append(grey)


read_img(IMAGE_PATH)
imgs = np.array(imgs, dtype='float32')

# 类别数量确定了输出层的神经元数量
num_classes = 10

# 批次大小
batch_size = 64

# 训练轮数
epochs = 6

# 确定输入图像的维度
img_rows, img_cols = 28, 28

# 使用load_data, 在导入数据时可以把测试集与训练集直接分开
(x_train, y_train), (x_test, y_test) = train_test_split(imgs[0 :], imgs[1 :], test_size=0.2)

# 使用backend后台的K.image_data_format()获取通道在通道中的位置是一个不错的选择
if K.image_data_format() == 'channels_first':
    x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)
    x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)
    input_shape = (1, img_rows, img_cols)
else:
    x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)
    x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)
    input_shape = (img_rows, img_cols, 1)

# 转换数据类型
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255

# 打印形状
print('x_train shape:', x_train.shape)
print(x_train.shape[0], 'train samples')
print(x_test.shape[0], 'test samples')

# 把一维的类别向量转化成二值向量形式
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)

# 初始化模型
model = Sequential()

# 逐层添加神经层
model.add(Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=input_shape))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(num_classes, activation='softmax'))

# 模型编译，定义了交叉熵损失，优化器和精度指标
model.compile(loss=keras.losses.categorical_crossentropy, optimizer=keras.optimizers.Adadelta(), metrics=['accuracy'])

# 开始训练
model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, verbose=1, validation_data=(x_test, y_test))

# 模型评估
score = model.evaluate(x_test, y_test, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])

# 模型保存
# MODEL_PATH + "/" +
model.save('duanchen_train.h5')

